A robust prognostic signature for hormone-positive node-negative breast cancer.
A robust prognostic signature for hormone-positive node-negative breast cancer.
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作者：
Griffith OL;Pepin F;Enache OM;Heiser LM;Collisson EA;Spellman PT;Gray JW
辅助治疗中的全身化疗可以治愈一些患者的乳腺癌，否则这些患者会因无法治愈的转移性疾病而复发。然而，由于只有一小部分患者在单独手术后会复发，因此挑战在于将高风险患者（从全身化疗中获益）与低风险患者（可以安全地避免治疗相关毒性和费用）分层。我们在这里集中在淋巴结阴性，ER阳性，HER 2阴性乳腺癌的风险分层。我们使用公开的微阵列数据集的大型数据库来构建随机森林分类器，并开发了一个强大的基于多基因mRNA转录的10年无复发生存预测因子，我们称之为随机森林复发评分（RFRS）。通过内部交叉验证、多个独立数据集以及使用受试者操作特征和Kaplan-Meier生存分析与现有算法进行比较来评估性能。使用k均值聚类确定特征的内部冗余，以定义具有较少数量的主基因的最佳特征，每个主基因具有多个替代。初始（全基因集）模型在训练数据上的内部OOB交叉验证报告了0.704的ROC AUC，与之前报告的或通过将现有方法应用于我们的数据集获得的ROC AUC相当或更好。定义了三个风险组，其概率截止值为低风险、中等风险和高风险。生存分析确定了这些风险组之间复发率的高度显着差异。对独立测试数据集的模型验证显示出高度相似的结果。还开发了较小的17-基因和8-基因优化模型，性能降低最小。此外，该签名被证明对接受治疗的患者和未接受治疗的患者几乎同样有效。RFRS允许灵活性的数量和身份的基因利用从数千到少至17或8个基因，每个有多个替代品。RFRS报告了与复发风险密切相关的概率评分。因此，该评分可用于将全身化疗专门分配给那些最有可能从进一步治疗中获益的高危患者。
Systemic chemotherapy in the adjuvant setting can cure breast cancer in some patients that would otherwise recur with incurable, metastatic disease. However, since only a fraction of patients would have recurrence after surgery alone, the challenge is to stratify high-risk patients (who stand to benefit from systemic chemotherapy) from low-risk patients (who can safely be spared treatment related toxicities and costs). We focus here on risk stratification in node-negative, ER-positive, HER2-negative breast cancer. We use a large database of publicly available microarray datasets to build a random forests classifier and develop a robust multi-gene mRNA transcription-based predictor of relapse free survival at 10 years, which we call the Random Forests Relapse Score (RFRS). Performance was assessed by internal cross-validation, multiple independent data sets, and comparison to existing algorithms using receiver-operating characteristic and Kaplan-Meier survival analysis. Internal redundancy of features was determined using k-means clustering to define optimal signatures with smaller numbers of primary genes, each with multiple alternates. Internal OOB cross-validation for the initial (full-gene-set) model on training data reported an ROC AUC of 0.704, which was comparable to or better than those reported previously or obtained by applying existing methods to our dataset. Three risk groups with probability cutoffs for low, intermediate, and high-risk were defined. Survival analysis determined a highly significant difference in relapse rate between these risk groups. Validation of the models against independent test datasets showed highly similar results. Smaller 17-gene and 8-gene optimized models were also developed with minimal reduction in performance. Furthermore, the signature was shown to be almost equally effective on both hormone-treated and untreated patients. RFRS allows flexibility in both the number and identity of genes utilized from thousands to as few as 17 or eight genes, each with multiple alternatives. The RFRS reports a probability score strongly correlated with risk of relapse. This score could therefore be used to assign systemic chemotherapy specifically to those high-risk patients most likely to benefit from further treatment.
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影响因子：
168.9
作者：
Wang, YX;Klijn, JGM;Foekens, JA
通讯作者：
Foekens, JA
影响因子：
11.2
作者：
Schmidt, Marcus;Boehm, Daniel;Gehrmann, Mathias
通讯作者：
Gehrmann, Mathias
影响因子：
64.8
作者：
Curtis, Christina;Shah, Sohrab P.;Chin, Suet-Feung;Turashvili, Gulisa;Rueda, Oscar M.;Dunning, Mark J.;Speed, Doug;Lynch, Andy G.;Samarajiwa, Shamith;Yuan, Yinyin;Graef, Stefan;Ha, Gavin;Haffari, Gholamreza;Bashashati, Ali;Russell, Roslin;McKinney, Steven;Langerod, Anita;Green, Andrew;Provenzano, Elena;Wishart, Gordon;Pinder, Sarah;Watson, Peter;Markowetz, Florian;Murphy, Leigh;Ellis, Ian;Purushotham, Arnie;Borresen-Dale, Anne-Lise;Brenton, James D.;Tavare, Simon;Caldas, Carlos;Aparicio, Samuel
通讯作者：
Aparicio, Samuel
影响因子：
14.9
作者：
Barrett T;Troup DB;Wilhite SE;Ledoux P;Evangelista C;Kim IF;Tomashevsky M;Marshall KA;Phillippy KH;Sherman PM;Muertter RN;Holko M;Ayanbule O;Yefanov A;Soboleva A
通讯作者：
Soboleva A
影响因子：
158.5
作者：
van de Vijver, MJ;He, YD;Bernards, R
通讯作者：
Bernards, R